Event discrete, process based simulation for Python.
SimPy is a process-based discrete-event simulation framework based on standard Python. Its event dispatcher is based on Python’s generators and can also be used for asynchronous networking or to implement multi-agent systems (with both, simulated and real communication).
Processes in SimPy are simple Python generator functions and are used to model active components like customers, vehicles or agents. SimPy also provides various types of shared resources to model limited capacity congestion points (like servers, checkout counters and tunnels). From version 3.1, it will also provide monitoring capabilities to aid in gathering statistics about resources and processes.
Simulations can be performed “as fast as possible”, in real time (wall clock time) or by manually stepping through the events.
Though it is theoretically possible to do continuous simulations with SimPy, it has no features that help you with that. On the other hand, SimPy is overkill for simulations with a fixed step size where your processes don’t interact with each other or with shared resources — use a simple while loop in this case.
The SimPy distribution contains tutorials, in-depth documentation, and a large number of examples.
SimPy is released under the MIT License. Simulation model developers are encouraged to share their SimPy modeling techniques with the SimPy community. Please post a message to the SimPy-Users mailing list.
A Simple Example
One of SimPy’s main goals is to be easy to use. Here is an example for a simple SimPy simulation: a clock process that prints the current simulation time at each step:
>>> import simpy >>> >>> def clock(env, name, tick): ... while True: ... print(name, env.now) ... yield env.timeout(tick) ... >>> env = simpy.Environment() >>> env.process(clock(env, 'fast', 0.5)) <Process(clock) object at 0x...> >>> env.process(clock(env, 'slow', 1)) <Process(clock) object at 0x...> >>> env.run(until=2) fast 0 slow 0 fast 0.5 slow 1 fast 1.0 fast 1.5
SimPy requires Python 2.7, 3.2, PyPy 2.0 or above.
You can install SimPy easily via pip:
$ pip install -U simpy
You can also download and install SimPy manually:
$ cd where/you/put/simpy/ $ python setup.py install
To run SimPy’s test suite on your installation, execute:
$ python -c "import simpy; simpy.test()"
Of course, you can keep working with SimPy 2:
$ pip install "simpy>=2.3,<3"
Documentation and Help
You can find a tutorial, examples, topical guides and an API reference, as well as some information about SimPy and its history in our online documentation. For more help, contact the SimPy-Users mailing list. SimPy users are pretty helpful. You can, of course, also dig through the source code.
If you find any bugs, please post them on our issue tracker.
Enjoy simulation programming in SimPy!
Changelog for SimPy
3.0 – 2013-10-11
SimPy 3 has been completely rewritten from scratch. Our main goals were to simplify the API and code base as well as making SimPy more flexible and extensible. Some of the most important changes are:
- Stronger focus on events. Processes yield event instances and are suspended until the event is triggered. An example for an event is a timeout (formerly known as hold), but even processes are now events, too (you can wait until a process terminates).
- Events can be combined with & (and) and | (or) to create condition events.
- Process can now be defined by any generator function. You don’t have to subclass Process anymore.
- No more global simulation state. Every simulation stores its state in an environment which is comparable to the old Simulation class.
- Improved resource system with newly added resource types.
- Removed plotting and GUI capabilities. Pyside and matplotlib are much better with this.
- Greatly improved test suite. Its cleaner, and the tests are shorter and more numerous.
- Completely overhauled documentation.
There is a guide for porting from SimPy 2 to SimPy 3. If you want to stick to SimPy 2 for a while, change your requirements to 'SimPy>=2.3,<3'.
All in all, SimPy has become a framework for asynchronous programming based on coroutines. It brings more than ten years of experience and scientific know-how in the field of event-discrete simulation to the world of asynchronous programming and should thus be a solid foundation for everything based on an event loop.
You can find information about older versions on the history page
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